Single image reflection removal using meta-learning

In recent years, reflection is a kind of noise in images that is frequently generated by reflections from windows, glasses, and so on, when you take pictures or movies. The reflection does not only degrade the image quality but also affects computer vision tasks, such as the accuracy of object detection and segmentation. In the task of single image reflection removal (SIRR), deep learning models play a key role for solving the problems of various patterns and the versatility. The challenge of SIRR is the influence of image quality and low precision of the method. We propose a deep learning model for the task of SIRR. The assumed scenes of reflection are varying, and there is little training data because it is difficult to obtain true values. We focus on the latter and propose an SIRR based on meta-learning. We adopt model agnostic meta-learning (MAML), and we propose an SIRR using a deep learning model with MAML, both of which are methods of meta-learning. The deep learning model includes the iterative boost convolutional long short-term memory network, which is adopted as the deep learning model. Experimental results show that the proposed method improves accuracy compared with conventional state-of-the-art methods.